Abstract
Despite enjoying extensive applications in video analysis, three-dimensional convolutional neural networks (3D CNNs) are restricted by their massive computation and storage consumption. To solve this problem, we propose a three-dimensional regularization-based neural network pruning method to assign different regularization parameters to different weight groups based on their importance to the network. Further we analyze the redundancy and computation cost for each layer to determine the different pruning ratios. Experiments show that pruning based on our method can lead to 2× theoretical speedup with only 0.41% accuracy loss for 3D-ResNet18 and 3.28% accuracy loss for C3D. The proposed method performs favorably against other popular methods for model compression and acceleration.